fsl

MEDx 3.4.3 User's Guide 34-1

34 FSL � FMRIB�s Software Library 34.1 INTRODUCTION

FSL � FMRIB�s Software Library - is a collection of functional and structural brain image analysis tools. This software was developed by the Image Analysis Group of the Oxford University Centre of Functional Magnetic Resonance Imaging of the Brain (FMRIB) under the guidance of Steve Smith. FSL is incorporated into MEDx to provide MEDx users with additional features and a powerful yet easy-to-use functional analysis tool. During fMRI analysis, the minimum number of specifications required from the user are entered into one compact, easy-to use graphical user-interface (GUI). Details concerning FSL beyond those provided in this chapter can be found under the FMRIB web site, http://www.fmrib.ox.ac.uk/fsl

34.2 OVERVIEW

The salient features of the FSL package are grouped under the Structural Analysis and FMRI Analysis menu items. Additionally various utilities developed for FSL are retained under the Miscellaneous Utilities option.

34.2.1 Structural Analysis Tools that do not utilize temporal information are grouped together under the Structural Analysis option. The Structural Analysis menu consists of an automatic Brain Extraction Tool (BET), FMRIB's Automated Segmentation Tool (FAST), FMRIB�s Linear Registration Tool (FLIRT), FMRIB's Utility for Geometrically Unwarping EPIs (FUGUE), and a noise reduction tool based on the Smallest Univalue Segment Assimilating Nucleus (SUSAN). Brain Extraction Tool (BET)

Currently there is no utility in MEDx, outside FSL, for the automatic deskulling of the brain. The MEDx Interactive Segmentation Module requires that the user specify a seed point and threshold parameters, making the results operator dependent. An automatic method obviates the need for user training and facilitates quantitative analyses through reproducible results. The default output of BET is an image with non-brain matter removed. Additional options exist which allow the user to delineate the surface of the brain on the original image and generate an exterior skull surface of the brain. FMRIB's Automated Segmentation Tool (FAST)

The FSL segmentation routine FAST automates three-dimensional segmentation while simultaneously providing bias field correction for spatial intensity variations. The segmentation tools currently available in MEDx, range from simple thresholding, to clustering, to region growing. and to the interactive segmentation tool summarized in the previous paragraph. The FAST method not only provides improvement in speed but also additional accuracy over existing

FSL - FMRIB's Software Library

34-2 MEDx 3.4.3 User's Guide

techniques due to a special hidden Markov random field formulation which is combined with an Expectation-Maximization algorithm. FMRIB�s Linear Registration Tool (FLIRT)

The FSL registration method FLIRT can provide an improvement in robustness over other existing registration routines in MEDx, namely the Automatic Image Registration (AIR) and the Statistical Parametric Mapping (SPM) registration tools. All three methods are capable of intra- and inter-modal linear registration, either to a different image, or to a template. The uniqueness of the FLIRT technique is its novel optimization method which determines the best registration by an efficient and robust minimization of the cost function, providing the MEDx user with a potential improvement in speed during registration FMRIB�s Utility for Geometrical Unwarping of EPI images (FUGUE)

Currently there is no utility in MEDx, outside FSL, for the geometric unwarping of brain images. FUGUE as implemented in MEDx also incorporates the implementation of the phase unwarping algorithm PRELUDE (Phase Region Expanding Labeller for Unwarping Discrete Estimates). The phasemaps unwrapped by PRELUDE are used by FUGUE for the generation of fieldmaps which are subsequently employed for the geometric unwarping of EPI images. Noise reduction based on Smallest Univalue Segment Assimilating Nucleus (SUSAN)

The FSL SUSAN routine provides a technique for reducing noise while maintaining the integrity of the image structure. Currently, MEDx has mean-median adaptive filters and low-pass filters for the purpose of reducing noise. The Gaussian smoothing used by the low-pass filter reduces noise at the expense of introducing blurring to the edges. The SUSAN method reduces the noise in a 2D or 3D image without compromising the underlying structure by only averaging a voxel with local voxels of similar intensity.

34.2.2 FMRI Analysis Tools pertaining to the determination and visualization of activation are grouped under the FMRI analysis option. The FMRI Analysis consists of FMRIB�s Easy Analysis Tool (FEAT), a Model-Free Inter-Repetition Variance Analysis (IRVA) option, a utility for Multivariate Exploratory Linear Optimised Decompostion into Independent Components (MELODIC), as well as Color Rendering, and SNR tools. FMRIB�s Easy Analysis Tool (FEAT)

The easy to use graphical-user interface (GUI) of FEAT allows specifications to be made with respect to pre-processing steps such as motion correction, spatial filtering, global intensity normalization, and temporal filtering from a single GUI. FEAT also contains two types of statistical analyses - the General Linear Model (the overall approach is similar to that of SPM) and a semi-model-free method which can prove to be useful when there is no a priori knowledge of the expected hemodynamic response. With FEAT, the final stages of the analysis can be rerun without rerunning any of the computationally intensive parts by simply directing the program to the directory containing the raw statistical



images. The previously existing tools in MEDx did not allow the user to specify all the requirements of the analysis from one GUI. The user had to make informed choices through separate menu items while conducting the analysis. The GUI simplification and the opportunity for automated decision-making provided by FEAT, enhance the already existing analysis tools in MEDx. Inter-Repetition Variance Analysis (IRVA)

The semi-model-free approach of IRVA refers to an analysis where no model of the hemodynamic response is required. IRVA is accessible both as a separate menu item, and also as one of the statistical options inside FEAT. The IRVA approach provides the MEDx user with an additional statistical analysis method. Multivariate Exploratory Linear Optimised Decomposition into Independent Components (MELODIC)

MELODIC is a model-free method which uses independent component analysis (ICA) to determine regions of significant activation in the brain. ICA subdivides the overall temporal response into a specified number of independent components. The identification of brain regions associated with the different temporal constituents, can be used apart from activation, for the detection and removal of artifacts in fMRI data. Currently MELODIC is the only utility in MEDx dedicated to independent component analysis Color Rendering

Color Rendering exists as part of the FEAT utility as well as separately. When Color Rendering is invoked as a menu item, one or two statistical images can be color-coded and superimposed onto the original image. (The Colorwash and Statistics Color Rendering utilities already in MEDx carry out similar, though not identical functions; the FSL tool is included to preserve the integrity of FSL.) Percentage Noise Analysis

The Percentage Noise Analysis calculates the temporal mean (µ) and standard deviation (σ) images, and then finds the mean and the median of the ratio σ/µ to provide a simple way of assessing the noise. There is an option for excluding regions that are not of any interest (such as outside the brain, and even at the brain edge)

Miscellaneous Utilities

The various FSL utilities are separated into three groups, which respectively refer to General, Group Operations, and Graphics/Output items. For most of these utilities, corresponding routines already exist in MEDx. Nonetheless the miscellaneous utilities are maintained for FSL integrity. Details of each utility and the corresponding MEDx alternative are explained in the body of the chapter.



34.3 STRUCTURAL ANALYSIS

34.3.1 Brain Extraction Tool (BET)1,2 There are many applications related to brain imaging which either require, or benefit from the ability to accurately segment the brain from non-brain tissue. In functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) accurate registration of activation images onto high-resolution MR images requires that non-brain tissue such as eyeballs, skin, and fat be removed from MR images prior to registration. Brain/non-brain segmentation is also essential for brain atrophy measurements which quantify brain volume changes with respect to some normalizing volume such as the skull or head size. Generation of an exterior skull surface or the elimination of non-brain matter enable a more accurate quantification of the changes.

A completely automatic method of brain extraction eliminates inter and intra-user variability, providing reproducibility as well as speed and efficiency. The outline of the brain surface can be delineated manually or through interactive region-growing algorithms which require the user to specify a seed point and various thresholds (see section 17.6). The time constraints, the need for training, and the lack of reproducibility work against interactive methods. Likewise, in semi-automated thresholding routines the user specified threshold has to be fine-tuned through experimentation. There exist more advanced methods that combine automatic thresholding with morphology operators such as erosion and dilation3 to help sever brain/non-brain tissue connection points e.g. narrow skull gaps and the optic nerve. The brain extraction algorithm in FSL (Toolbox->FSL ->Structural Analysis->BET Brain Extraction) belongs to another class of sophisticated models which utilize deformable surfaces.

The BET technique finds the brain surface by adjusting the vertices of a tessellated sphere. Initially a rough distinction is made between brain and non-brain matter by thresholding at 10% of the way between 2% (t2) and 98% (t98) points of the signal intensity histogram. The brain/background threshold is then used to approximately estimate the position of the center of gravity (COG). The COG subsequently serves as the center of the tessellated spherical surface. The radius of the tessellated sphere is set to half of the estimated brain/head radius to allow the surface to grow to the optimal estimate. In finding the surface, each vertex is moved through iterative incremental surface updates taking into consideration the degree of smoothing. Although it is desirable to smooth high surface curvature more than low curvature, too much smoothing can adversely affect low curvature regions. So, a nonlinear function is used to smooth points proximal to the surface more heavily. The vertices of the new tesselated surface

1 Smith S. "BET: Brain Extraction Tool" (submitted to NeuroImage) 2 http://www.fmrib.ox.ac.uk/analysis/research/bet 3 Lemieux L., Hagemann G., Krakow K, Woermann F.G. (1999). Fast accurate, and

reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Magn.Reson.Med. 42:127-135.



are then calculated taking into consideration the local minimum (Imin) and maximum intensity (Imax) values along with the local intensity threshold tl where

tl = (Imax-t2)*bt + t2 (1)

The Fractional intensity threshold bt of Eq. 1 can be modified from the GUI (Figure 34-1). A modification is seldom necessary though, as the default value of 0.5 usually provides excellent results. Deviations from the default value of 0.5 cause the overall segmented brain to become larger (<0.5) or smaller (>0.5). The threshold tl is then used to calculate the update �fraction�

(2) BET is considered to be completely automated since the Fractional intensity threshold and the related Intensity gradient threshold bf rarely need to be adjusted. The Intensity gradient threshold bf, when not 0, allows bt to vary along the slice selection direction leading to a position dependent modification of the surface selection criterion. Once the brain surface is located, a search is conducted outwards from the brain surface to locate the exterior of the skull.

The most commonly used features of BET (Figure 34-1) are non-brain matter removal, i.e. Generate image with non-brain matter removed, and the superposition of the estimated brain surfaces onto the brain, i.e. Generate image with estimated brain surface overlaid on original options. Additional options are for masking, i.e. Generate binary brain mask image, thresholding, i.e. Threshold segmented brain image (and mask if required), and skull surface generation, i.e. Generate exterior skull surface image are regularly hidden under Advanced Options along with the Fractional intensity threshold and Intensity gradient threshold entries.

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Figure 34-1. The Brain Extraction Tool Graphical User Interface

The thresholds should not be modified except for the rare occasion where the results are sub-optimal. Deskulled images apart from being essential for volume quantification and certain registration applications, lend themselves to impressive 3D volume renderings.

34.3.2 FMRIB's Automatic Segmentati Tool (FAST)4,5 Segmentation of brain imaging applications extend beyond the brain versus non-brain issues covered by BET. The differentiation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is essential for the evaluation of perfusion or volume related measurements per tissue type. Accurate segmentation results require the elimination of inconsistent contributions due to interactive user specifications. The 3D FAST segmentation technique is entirely automatic and provides enhancement in speed as well as correction for bias resulting from magnetic field nonuniformities.

Segmentation methods can be roughly subdivided into structural and statistical methods. The former consist of edge detection techniques and region growing algorithms. The performance of structural methods is affected by how well defined the regions are. In contrast statistical methods assign each pixel a probability value which reflects the likelihood of that pixel belonging to a specific tissue category. While parametric methods assign a certain functional form to this probability e.g. Gaussian, non-parametric methods determine the functional form from the data without any a priori assumptions. The FAST technique (Toolbox->FSL->Structural Analysis->FAST Segmentation)

4 http://www.fmrib.ox.ac.uk/analysis/research/flirt 5 Zhang Y, Brady M, Smith S. (2001). Segmentation of Brain MR Images Through a Hidden

Markov Random Field Model and the Expectation Maximization Algorithm. IEEE Trans. Medical Imaging 20:45-57.



employs a statistical parametric algorithm based on a novel hidden Markov random field (HMRF) formulation. Unlike finite mixture (FM) models which are mostly parametric and rely on histograms, FAST incorporates spatial information directly by taking into consideration interactions of neighboring sites. With the FAST technique, the HMRF model is subsequently fit through an Expectation-Maximization (EM) algorithm. The HMRF-EM framework is robust and flexible allowing the incorporation of other algorithms such as the intensity bias correction method of Guillermaud and Brady6.

Statistical segmentation techniques can be differentiated based on (i) the segmentation class probability distribution model, (ii) the algorithm adopted to fit the model, and (iii) the classification method. The FAST technique is based on the HMRF model. The signal intensity distribution in FAST is assumed to be Gaussian although this assumption is not an integral part of HMRF formulation. In FAST, the segmentation classes constitute the hidden MRF which are estimated from the observed pixel intensity distributions p(y/x) where y refers to the pixel intensity distribution and x refers to the hidden segmentation class. The MRF theory allows spatial information to be encoded through constraints imposed on neighboring pixels which are assumed to have similar intensities. This assumption turns out to be more robust than that of FM models which assume total independence of neighboring pixels by using histograms, indirectly implying that similar pixel intensities in different parts of the brain are associated with similar structural shapes. The FAST technique also provides bias correction whereby the bias field is incorporated as an additional hidden parameter p(y/x,B). The estimation of the bias field is strongly dependent on the segmentation classification. In FAST, the parameters of the HMRF model are determined using the EM algorithm. In this process, the initial values for each parameter can be specified explicitly or through an automated K-means starting parameter estimation algorithm. The class labels for each pixel are subsequently determined through an adaptation of the maximum aposteriori (MAP) algorithm.

The FAST technique is designed for MR applications (Figure 34-2). When the segmentation is carried out based on the contrast of a single volume, it is essential for the user to specify the correct Image Type which can be T1-weighted, T2-weighted or Proton density (PD). Alternately, the segmentation can be carried out utilizing the data from a Number of input channels. In multichannel segmentation, a maximum of 5 different channels can be specified. Each channel brings out a different contrast of the same volume. The different contrasts are not necessarily constrained to T1, T2, or PD images, but can also be perfusion maps, diffusion maps or magnetization transfer images. In the Output options, when the Number of classes is set to 3, the brain is segmented into GM, WM, and CSF. However, when there are lesions in the brain or when the user is solely interested in CSF, the Number of classes can be changed to 4 or 2, respectively. The number of Output images produced at the end of the analysis depends on the Binary segmentation specifications. With All classes in one image, the results of segmentation are summarized in one image with a different number assigned to each class label (GM=2; WM=3, CSF=1). When

6 Guillemaud R. Brady J.M. (1997). "Estimating the bias file of MR images" IEEE Trans Med.

Imaging 16:238-251.



there is One image per class, a segmentation output exists for each class such that the pixels within the selected class have values of 255 while the rest are 0. In the case of Probability maps, there is a continuum of values for each segmentation output. The values reflect the likelihood of each pixel belonging to the designated segmentation class such that the sum of the probability maps equals 1. When a pixel has a relatively high probability value for both WM and GM segmentation classes, the implication is that both types of tissues exist within the same voxel. The Percentage volume maps are further estimates of the volume occupied by the different segmentation classes. The sum of the percentage volume maps is also equal to 1. The Probability maps and the Percentage volume maps provide similar but not identical information. The Probability maps take into account neighborhood information in addition to percentage volume prior to assigning a certain pixel to a particular segmentation class. Irrespective of any specification, there is always a bias field correction in FAST. The Bias field and Restored input selections allow the visualization of the bias field and the corrected image for each input specification.

Additionally there are Advanced options for spatial normalization in FAST. When Use a priori probability maps is selected, the images are initially normalized to the Montreal Neurological Institute (MNI) template and the initial parameters of classes are estimated using standard tissue-type probability maps instead of using the K-means algorithm. When a single channel is only available, the initial class parameter values can also be specified through the Use file of initial tissue-type means. When the segmentation results are deemed subpar, it may be worthwhile to do a 2D segmentation to disregard a strong bias field component along the z-dimension that may not have been properly corrected for.

Figure 34-2. The FMRIB's Automatic Segmentation Tool (FAST) Graphical User Interface



34.3.3 FMRIB's Linear Registration Tool (FLIRT)7 Registration is important in functional imaging as signal variations from subject motion can dominate those resulting from the hemodynamic response. In addition, the superposition of activation onto higher resolution structural images, intersubject and intermodality comparisons all require registration. The registration problem can be stated as finding the best alignment between a test and reference image. Commonly the intensity information at each voxel is used to perform the registration. There are two main classes of registration: intra-modal and inter-modal. In inter-modal registration, the contrast between different tissue types can change such that the mapping between the intensity values is unknown. The mathematical formulation of accomplishing registration is expressed in the form of the optimization of a cost function. Thus, registration techniques can be differentiated based on the cost functions they employ. A variety of registration methods already exist in MEDx, ranging from simple realignment through rigid body motion correction, to intermodality coregistration and spatial normalization.

34.3.3.1 COMPARISON OF DIFFERENT REGISTRATION MODULES

The multiple registration packages in MEDx are briefly summarized below. Under Toolbox->Registration, the Surface registration program uses a least squares surface fitting algorithm consisting of scaling, rotation, and translations to realign two similar data sets. The AIR registration program has several applications whereby rotations and translations are used for regular realignment, and different linear and nonlinear models are used for intermodality and intersubject registrations. In the latter case, the images are registered to a template or to one another. The principle of AIR is to minimize the variance of the pixel-to-pixel ratio of the images that are being registered. The Talairach Atlas option, which is available only in 3D mode, registers the specific landmarks of the current volume to those of the Talairach and Tournoux template. The Warp program, which is available only in 2D mode, can warp and join the two regions of an image along the specified marks. The number of points chosen by the user, the polynomial order, and the method of interpolation dictate the quality of the result.

7 http://www.fmrib.ox.ac.uk/analysis/research/flirt



Figure 34-3. The FLIRT Registration Graphical User Interface

Alternate registration routines are located in Toolbox->SPM Modules, under Realign, Coregister, Spatial Normalize (see sections 32.2- 32.4). With the SPM method, the realignment is based on a rigid-body transformation consisting of three translations and three rotations. The SPM coregistration module partitions the images into regions of gray matter (GM) and white matter (WM) and registers the respective partitions. Template mapping in SPM is implemented through a global non-linear warping model. The FLIRT Linear Registration method located in Toolbox->FSL->Structural can realign and spatially normalize images using models of 6 to up to 12 degrees of freedom (DOF). The distinguishing feature of FLIRT is the speed it provides through a novel optimization of the cost minimization. The FLIRT GUI is displayed above in Figure 34-3.



34.3.3.2 GENERAL8

The heart of the FLIRT method is its optimization. An optimization method searches through the possible transformations to find the one which minimizes the cost function i.e. maximizes the quality of the registration. Some of the standard mathematical optimization methods such as Gradient descent and Powell's method find local minima. Others such as Simulated Annealing and Full search find global minima. In FLIRT a combination of Full search and the Powell's Method is used to attempt to find the global minimum in a reasonable amount of time. The basic idea behind Powell's Method is to break the N dimensional minimization down into N separate 1D minimization problems. Then, for each 1D problem a binary search is implemented to find the local minimum within a given range. On subsequent iterations, the best directions to use for the 1D searches are estimated, enabling efficient navigation along narrow "valleys". Since a bad starting point for Powell's Method can lead to many local minima, a separate search is also conducted by FLIRT through the parameter space (one parameter for each DOF) to find optimal starting points. The mesh spacing determines the amount of time required to perform the search. For an affine transformation of 12 DOF however, even a relatively coarse mesh requires too much time to search. Therefore, a multi-resolution approach is also adopted where the approach is to initially find the best starting point in the lowest resolution and then progressively refine this at higher and higher resolutions, 8mm3-> 4mm3-> 2mm3->1mm3, provided the resolution is not increased beyond that of the initial image. Despite the speed increases achieved using lower resolutions and adaptive step sizes, a full search is impractical. So, initially the local minima are identified, without optimizing the local search. A second search is then conducted through the local minima, to find the globally optimal translation and global scale. Once the optimum registration parameters are determined, they are applied to the images.

34.3.3.3 MODEL/DOF

Registration methods can be distinguished from each other based on the transformations they employ. Across Model/DOF, the various registration options available in FLIRT are: Rigid Body (3 parameter model), Rigid Body (6 parameter model), Global Rescale (7 parameter model), Traditional (9 parameter model), and Affine (12 parameter model). Rigid Body (3 parameter model) is for 2D registration and consists of 2 translations and 1 rotation. The Rigid Body (6 parameter model) consists of 3 translations and 3 rotations. The rotations about the x, y, and z axes are referred to as pitch, roll, and yaw, respectively. A Global Rescale transformation includes all rigid body transformations as well as a single scale parameter increasing the DOF to 7. Such transformations preserve all angles and relative lengths. A Traditional transformation consists of 3 translations, 3 rotations, and 3 scalings with a total of 9 DOF. Finally an Affine transformation includes all rigid body transformations as well as individual axis scaling and skewing in all three directions, giving it a total of 12 DOF. Such transformations do not preserve angles or lengths, but do preserve straight lines.

8 Jenkinson M, Smith S. (submitted) Optimisation in Robust Linear Registration of Brain

Images. Medical Imaging



34.3.3.4 SEARCH ANGLES AND REPRESENTATIONS

Under Advanced Options, the choice also exists in FLIRT for specifying the extent of angle Search. For images that are grossly misaligned i.e. more than 90 degrees of rotation, Images under the Search option, should be set to Incorrectly Oriented. This automatically extends the search to between �180 and 180 degrees. The Already virtually aligned (no search) option eliminates the search. The default Not aligned, but same orientation option constrains the search to between �90 and 90 degrees. The program is robust enough that images rotated by 80 degrees can be successfully registered onto the original even when all search angles are set to 0, provided the Correlation Ratio cost function is used. The rotations are internally represented as Euler angles.

34.3.3.5 COST FUNCTION

A cost function rates the quality of an alignment by assigning a value to it such that poor alignments have higher costs and good alignments have lower costs. In FLIRT, the Cost Function options are (Figure 34-3):

! Correlation Ratio

! Mutual Information

! Nomalised Mutual Information

! Normalised Correlation (intra-modal)

! Least Squares (intra-modal)

The Normalised Correlation is the simplest cost function. Given the reference image and the transformed test image, it calculates the correlation over the set of all voxels. This method however assumes that the unknown mapping between the intensities of the two images is linear, which is not always true, e.g. T1 and T2 weighted images. Similarly the Least Squares is also based on the premise of linearity. The quality of the registration is judged by minimizing the least squares error between the reference image and the transformed test image. The assumption of linearity constrains these two functions to be intra-modal as the nature of contrast is bound to change across modalities. In MR applications, the linearity requirement further restricts the registration to images of the same type i.e.T1, T2 etc. The cost functions which can handle non-linear intensity dependencies are Correlation Ratio and Mutual Information. In Correlation Ratio calculations, the reference image is partitioned into areas of similar intensity, i.e. isosets. The boundaries of these areas are then placed over the (transformed) test image. The variance within each area is calculated and the cost is defined as a weighted sum of the variances divided by a normalization term. The specific cost function is:

Correlation Ratio CR = (weighted sum of Var (Si) ) / Var (S) where Si is the ith isoset. The normalization is global for CR.

The most general cost function is Mutual Information. The definition of Mutual Information is based on the entropy and joint entropy of the individual images. Mutual information is defined as:

MI = H(X) + H(Y) � H(X,Y) (3)



where the entropy H = sum (-p log(p) ) with p referring to the appropriate histogram. For the case of Normalised Mutual Information, the cost functions is slightly different,

MInorm = ( H(X) + H(Y) ) / H(X,Y)

such that the sum of entropies in each direction is divided by the joint entropy. The Number of Histogram Bins entry under the Cost Function notebook page is quite important for entropy histogram calculations. Of the methods discussed above, the default Correlation Ratio method is strongly advised as it has proven to be the most robust in practice.

34.3.3.6 INTERPOLATION

Interpolation is an essential part of the registration process due to the unavoidable discretization process. The required translation may be sub-pixel and rotations can almost never be accomplished by whole pixel shifts. In its most general form, interpolation is the fitting of a continuous surface to a discrete lattice of points. In interpolation methods where the fitted function is required to have continuous first and higher order derivatives, spline functions are used. The interpolation methods provided in FLIRT are:

! Tri-Linear

! Nearest Neighbour

! Sinc

The Nearest Neighbour and Tri-Linear methods are local interpolations which determine the pixel value taking into consideration the values of the neighboring pixels within a plane and volume, respectively. They do not guarantee the continuity of first and higher order derivatives. The Sinc interpolation further adds a distance-dependent weighting during the calculations. The user can specify the Width of Sinc Window. The Sinc Window Options are: Rectangular, Hanning, and Blackman.

34.3.3.7 WEIGHTING VOLUMES

The option of Weighting Volumes further allows the cost function to be weighted differently at each voxel. The prescribed weightings can be specified across Reference weighting and Input weighting entries when the Mode is set to Input Image/Group -> Reference image. For a Mode of Low Res Image/Group -> High Res Image/Group, the corresponding weightings are specified across High Res weighting and Low Res weighting. The cost function weighting allows the exclusion of areas of no interest and increased contribution from structures such as the ventricles. The weighting of cost function differs from masking in that it does not introduce artificial boundaries.

34.3.3.8 REGISTRATION MODES

It is possible to run FLIRT on Images or Groups under two different modes. With the Input Image/Group->Reference Image Mode option, the Input Image/Group is registered onto the template Reference Image. With the Low Res Image Group->High Res Image/Group->Reference Image Mode option, the Low Res Image/Group, is initially registered onto the structural High Res Image/Group which in turn is registered onto the Reference Image template.



The Secondary images are useful for putting Statistics images into structural or Talairach space. Depending on the specification for the Number of Secondary Images/Groups to apply combined transform to, additional entries appear for Secondary Image/Group. The secondary images are not used in the actual registration process. They are simply aligned to the template reference image using shadow transforms. When the Mode is Input Image/Group -> Reference Image, the transformation applied to register the Input Image/Group to the Reference image is applied to the secondary images. When the Mode is Low Res Image/Group ->High Res Image/Group -> Reference Image, the registration transformations are concatenated. A summary of the sequence of operations for the Low Res->High Res->Standard mode registration with secondary images is:

1. Register the Low Res to the High Res Image.

2. Register the High Res Image to the Reference Image.

3. Concatenate the above transforms

4. Apply the resulting transform to the Low Res Image and Secondary Images

Multiple registrations can be performed in the same session by specifying Groups instead of Images. Again the above steps are followed. The registration is done between the first High Res and first Low Res (and Reference) followed by the second High Res and second Low Res etc. The Secondary images are also expected to be in Groups. To illustrate this with an example, if the output of three different subjects consisting of two statistics images each e.g. Z-score and p-values, is to be put into Talairach space, the formulation would be:

Reference Image: Talairach template image

High Res Image/Group: {Structural A, Structural B, Structural C)

Low Res Image/Group: (EPI Vol A, EPI Vol B, EPI Vol C)

Secondary Image/Group 1: (Z-score A, Z-score B, Z-score C)

Secondary Image/Group 2: (p-value A, p-value B, p-value C)

To be able to repeat the registration or to transform additional statistical images at a later date, the transforms should be saved using the Save Shadow Transform option. When applying saved transforms, the Apply Shadow Transform option should be used.



34.3.4 FMRIB's Utility for Geometric Unwarping of EPI images (FUGUE)9 Geometric distortions in echo-planar images arise from magnetic field inhomogeneities introduced by the shim coils and from the sample. The magnetic field inhomogeneities are particularly pronounced around the sinuses in the frontal region and can interfere with the accurate depiction of the brain, hampering the assessment of activation in response to cognitive tasks. The warping arises from residual magnetic fields which cause pixels to be displaced from their original location upon reconstruction. A retrospective correction for the displacement is possible through a B0 field map which can be calculated from two images acquired using asymmetric echoes10,11.

The FUGUE implementation in MEDx (Figure 34-4), unwarps EPI images similarly, using a field map derived from a pair of unwrapped phase images. The user can Load Unwrapped Phase Maps directly. It is also possible to specify Wrapped Phase maps along with the Unwrap Phase Map instruction whereby the unwrapping is carried out prior to geometric unwarping, using PRELUDE, the acronym for the fancy "Phase Region Expanding Labeller for Unwarping Discrete Estimates". The ultimate goal of FUGUE is to make structural and functional EPI images comparable to each other. In the process of unwarping, the user has the option to Generate B0 Field Map and Generate Shift Map and save the results in Output B0 Field Map and Output Phaseshift files respectively. The B0 field map is obtained by dividing the difference of the phase maps with the Field Map Asymmetry Time (ms):

B0 map = (phase 1 � phase2) / Field Map Asymmetry Time (ms) (4)

The pixel shift map can be calculated from the total residual accumulation in phase during the EPI acquisition. The phase is accumulated during the readout period in between phase encodings (PEs), i.e. during Dwell time (us). So the duration of the acquisition is roughly equal to the product of the number of phase encodings (PEs) and Dwell time (us). The shift map can also be obtained by multiplying the field map by the duration of the acquisition and dividing by 2π.

Shift map = (B0 map) * (# of PEs) * ( Dwell time ) / 2π (5)

The Dwell time (us) and the Field map Asymmetry Time (ms) can be specified from the Acquisition page under Advanced Options. The Advanced Options also incorporate specifications pertaining to Warping and Smoothing.

34.3.4.1 UNWARPING METHODS

The unwarping of images can be accomplished using the Phase Conjugate method, the Voxel Shift with Intensity Correction option, or through the

9 http://www.fmrib.ox.ac.uk/analysis/research/fugue

10 Jezzard P, Balaban R. S. (1995) "Correction for Geometric Distortion in Echo Planar Images from B0 Field Variations. Magn. Reson. Med. 34:65-73.

11 Munger P, Crelier GR, Peres TM, Pike GB. (2000) "An Inverse Problem Approach to the Correction of Distortion in EPI Images", IEEE Trans. Med. Imag. 19:681-689.



default Voxel Shift option in any of the x, y, z Warp Directions. The Phase Conjugate method uses the information in the B0 map to correct for the phase errors in the EPI image10. The Voxel Shift with Intensity Correction option is an iterative technique based on the conjugate gradient (CG) method11 which also corrects for intensity variations through field gradient calculations. The CG method although beneficial at higher field offsets of 40-80 Hz, can fail reconstruction when large negative B0 field gradients are present. Negative gradients are associated with compression while positive gradients are associated with stretching in the distorted image. The Voxel Shift with Intensity Correction method can also suffer from noise of the B0 gradient images which can offset the benefit of intensity correction. There are no B0 gradient calculations in the CG variant implemented in the Voxel Shift method.

Figure 34-4. The FUGUE User Interface.

34.3.4.2 SMOOTHING

Regularisation of the B0 field through a smoothing filter has been found to improve unwarping. In 2D Gaussian Smoothing, a Gaussian Std Deviation value of 1 has been shown to provide the best results12. Similarly for 3D Gaussian Smoothing, a Gaussian Std Deviation value of 1 is advocated. With Median Filtering, the user has the option of specifying the Median Width. The

12 Jenkinson M. (2001) Improved Unwarping EPI Volumes using Regularised B0 Maps". Human

Barin Mapping conference, Brighton.



2DDespiking Filter option requires the specification of a Despiking Threshold. In the 3D Polynomial Fitting case, the Degree of Polynomial to be fitted has to be specified. The Pooled Adjacent Violators Algorithm (PAVA), is a statistics used to enforce monotonicity i.e. a one-to-one warp. In general, it has been found that 2D Gaussian Smoothing outperforms the rest of the options including performing no regularization at all12. However for Median Filtering and low order 3D Polynomial Fitting, regularization with Gaussian smoothing has caused the results to deteriorate. So, it may be best not to use heavy smoothing in practice.

34.3.5 SUSAN - Nonlinear Noise Reduction using Smallest Univalue Segment Assimilating Nucleus13,14 Noise can be introduced into a digitized image in many ways. The noise reduction technique based on the Smallest Univalue Segment Assimilating Nucleus (SUSAN) uses nonlinear filtering to reduce noise in a 2D or 3D image while preserving the underlying structure. Although many �structure preserving� filters have achieved some degree of success especially in preserving one-dimensional image structure (e.g. median), very few have successfully preserved two-dimensional image brightness structure, such as corners and junctions. The smoothing algorithm of SUSAN retains the integrity of the image structure within 2D or 3D images.

34.3.5.1 MEDX SMOOTHING OPTIONS

Multiple filters have been implemented in MEDx for the purpose of reducing noise. These are, apart from SUSAN, the Low Pass filter located in Toolbox->Filtering->Frequency Domain, the Mean and Median Adaptive Filters located under Toolbox->Filtering->Neighborhood Operations and the Gaussian Smoothing located under Toolbox->Functional->Filtering. Low pass filters, remove from the image the high frequency content e.g. sharp transitions in signal intensities such as edges. Thus by definition low-pass filters cause blurring. The Mean filter, calculates at each voxel location, the average brightness value of the neighboring voxels. The Gaussian filter is similar to the mean filter, except that the values of the neighboring voxels are given different weighting as defined by a spatial Gaussian distribution. The greater the standard deviation of the Gaussian, the larger the smoothing effect, yet the less accurate the localization of the edge is. In general, linear filters create more blurring of image details than do most non-linear filters. With non-linear filters, the neighboring signal intensity values of each voxel are usually sorted into ascending order. The median filter, the simplest non-linear filter, uses the central value in the ordered list for the new value of brightness. When the filter is adaptive however, the voxel value is only replaced when certain threshold criteria are met. The Median Filter is much better at preserving straight edge structure than Gaussian smoothing, and is especially good at reducing impulse noise i.e. "salt and pepper" noise. The SUSAN Nonlinear Noise Reduction

13 http://www.fmrib.oc.ac.uk/fsl/susan/index.html 14 Smith SM. (1997) Method for digitally processing images to determine the position of edges

and/or corners therein for guidance of unmanned vehicle. UK patent 2272285.



technique located in Toolbox->FSL->Structural, accomplishes the noise reduction by averaging each voxel with local voxels of similar intensity.

34.3.5.2 GENERAL

The SUSAN principle associates with each image point a local area of similar brightness. The SUSAN noise filtering algorithm preserves image structure by smoothing only over those neighbors which form part of the "same region" as the central voxel. This local area is referred to as the "Univalue Segment Assimilating Nucleus" (USAN) and contains a lot of information about the structure of the image. The USAN area is determined by placing circular masks or equivalent digital approximations of Gaussian weighting onto the image. The brightness of each voxel within the mask is compared with the brightness of the nucleus of that mask. Voxels whose brightness falls within a specified range of the center brightness value, make up the USAN. SUSAN accomplishes the noise reduction by averaging each voxel with local voxels in its USAN i.e. with voxels of similar intensity. The number of USAN voxels decrease at edges due to sharper gradients in signal intensity values. So, minimum averaging takes place at edges, making SUSAN relatively immune to blurring effects. When the USAN area is zero, as in the case of impulse noise, the median of the eight closest neighbors is used to estimate the correct value of the voxel.

34.3.5.3 NOISE REDUCTION SPECIFICATIONS

The area of the USAN, which is essential for the correct implementation of SUSAN, can be determined from the image that is being smoothed or from a user-specified image (Figure 34-5). In either case, the Brightness threshold is crucial as it determines which voxels constitute the local area of similar brightness. The user needs to estimate this threshold for optimal performance � it should ideally be larger than the noise level and smaller than the contrast of edges which are to be preserved. The outer boundaries of the mask used for the calculation of the USAN are determined by the Mask SD or mask standard deviation specification in mm which roughly corresponds to the half width half maximum of a Gaussian. The USAN can be equivalently determined from a user-specified image by setting Separate images to find USAN from entry to 1 or 2. In this case, new options come up for USAN image specification and the Brightness threshold has to be separately specified for each. When there is more than one USAN image, it is required that a particular voxel within the neighborhood be close in intensity to the central voxel for both USAN-input images. In the case of extreme noise, i.e. when the USAN area is empty, again the default is to calculate the voxel value from the median of the closest neighbors. When Use median when no neighborhood is found option is de-selected however, no smoothing takes place for those voxels that have an empty USAN. The SUSAN noise filtering technique can work on any image or volume consisting of 16 bits. The duration of the smoothing depends on the maximum possible size of the USAN as specified in the Mask SD specification. In general, with SUSAN, the smoothing is accomplished in a reasonably short time.



Figure 34-5. The SUSAN noise reduction GUI

34.4 FMRI ANALYSIS

34.4.1 FEAT � FMRIB's Easy Analysis Tool15 A complete fMRI analysis is indeed a "feat" consisting of pre-processing steps, statistical analyses, and comparison of the analyses between different subjects and sessions. FEAT accomplishes all this in a single run based on the specifications in a compact GUI located in Toolbox->FSL->FMRI Analysis->FEAT. The FEAT menu provides options for different types of experiments. The FILM model-based option (Figure 34-6) is a sophisticated implementation of the General Linear Model (GLM) � thus the overall approach here is similar to that of SPM (Statistical Parametric Mapping) of Friston et al16. This option can be used for block or single-event designs when enough is understood about the expected responses for a full model-based analysis. The design matrix of the experiment can be generated using the Simple model setup or Full model setup options. While both setups can handle block and event-related analyses, the simple model requires all timings to be regularly spaced. The stimulus onset times for randomly spaced blocks or single events have to be specified from a custom-designed file using the full model. An alternative analysis method is the

15 http://www.fmrib.ox.ac.uk/fsl/feat3/index.html 16 Friston KJ, Holmes A, Worsley K, Poline J-B, Frith C, Frackowiak R (1995) Statistical

parametric maps in functional imaging.. A general linear approach. Hum Brain Mapping 2:189-210.



IRVA semi-model-free approach which requires no a-priori knowledge about the experiment (Figure 34-7). The semi-model-free approach which is based on inter-repetition variance analysis (IRVA) comes in two varieties: the Cyclic IRVA is designed for regularly spaced stimulations, while the Triggered IRVA is normally reserved for irregularly spaced experiments. The semi-model-free analysis can also be independently accessed through the Toolbox->FSL->FMRI Analysis->Inter-Repetition Variance Analysis menu and is discussed in a subsequent subsection.

34.4.1.1 EXPLANATORY VARIABLES

In a general linear model analysis, the goal is to model the stimulus response with different waveforms, i.e. explanatory variables (EVs). In the most simplistic approach, a single explanatory variable is assigned to each stimulus type. An example is the ABAB approach of Simple model setup (Figure 34-8) where the duration in seconds of the A(rest) period and B period determines the general shape of the explanatory variable. Upon pressing on the Process button, the final form of the explanatory variable subsequent to convolution and temporal filtering is displayed in the first column of the design matrix. Similarly, in the ABACABAC approach, the A(rest) period, B period, and C period have to be specified in seconds prior to displaying the design matrix. The two explanatory variables associated with the stimuli "B" and "C" in this case occupy the first and third columns of the design matrix. The second and fourth columns correspond to the temporal derivatives.

Figure 34-6. The FEAT GUI for model-based FILM



Figure 34-7. The FEAT GUI for semi-model-free IRVA.

The Full model-setup allows the user to view the details of these default selections to make further modifications as deemed necessary (Figure 34-9). In this particular case, the 24 s specified for the A (rest) period and B period correspond to the Off and On entries, respectively.

Figure 34-8. The Simple model setup for ABAB

There is no Phase lag and the waveform commences right from the start i.e. Skip is 0. This does not imply however that no acquisitions were eliminated. It is the Delete volumes entry that determines the number of scans to be eliminated prior to reaching steady-state. For a simple paradigm of On/Off stimulation, Number of EVs is automatically set to 1.



Figure 34-9. The Full model setup associated with Figure 34-8

For more complex cases, the number of explanatory variables in the Design matrix is determined by the number of different effects that the user wants to model � one for each modelled stimulation type, and one for each confound. Each explanatory variable can then be custom designed by going to that particular EV page. Factors that contribute to the design of the EV waveform are Basic shape, Convolution with the hemodynamic response function (HRF), Apply temporal filtering and Add temporal derivative. The Basic shape refers to the form of the waveform prior to convolution and can be Square, Sinusoid or custom-designed. The Square and Sinusoid waveforms start after the Skip period and Stop after the specified duration. A -1 for Stop after indicates that the user does not want to curtail the waveform at any point. The Square wave starts with a full Off period followed by the On period and the Sinusoid starts by a negative cycle of the specified Period. The waveforms can be brought forward by the specified Phase shift. Note that all waveform specifications are



in seconds. The equivalent number of volumes is then calculated taking into consideration the TR. The custom designed waveforms are especially suitable for single-event experiments with irregular timing. The Custom (1 entry per volume) expects an ASCII file consisting of a list of numbers, separated by spaces or newlines, with one number for each volume. The numbers can be all 0's or 1's or can take on a range of values. The Custom (3 column format) expects the waveform to be defined through triplets of numbers. The first number is the onset (in seconds) of the period; the second number is the duration (in seconds) of the period and the third number is the value of the input during that period, For example in a study with a TR of 3, the line 58 70 1 would imply that acquisitions 58 to 70 correspond to activation. For Number of EVs of greater than 2, an additional Basic shape waveform, Interaction, exists The user can specify between which EVs the interaction is being sought whereby the Interaction output is produced by multiplying together the selected EVs. The origin of the Basic shape waveform is expected to coincide with the start of the first image taken after the deleted scans. Scans are deleted based on the Delete volumes entry such that scans acquired prior to reaching steady-state are eliminated. Delete volumes is not for the correction of the time lag between stimulation and measured response. The delay is modelled by convolving the basic waveform with the hemodynamic response function (HRF). The hemodynamic response models available under Convolution in Advanced are Gaussian, Gamma variate, and SPM99 HRF. The default Gamma variate option can be designed through the Stddev and the Mean lag. specifications. The details of the Gaussian waveform are modelled through the standard deviation Sigma and the Peak lag specifications. The more sophisticated SPM99 HRF waveform is a preset function with a mixture of two Gamma functions � a standard positive function at normal lag and a small delayed inverted gamma variate which attempts to model the undershoot. If the original waveform is already sampled from the data itself, the None option should be selected for Convolution. When the difference between the different Convolution settings is not clear, the default setting of Gamma variate should be used. For the case of more than one EV, orthogonalization options also exist through the Orthogonalise WRT EVs selections. The result of the convolution can be further modified by using the Apply temporal filtering option. Normally, the same temporal filtering should be applied to model and data since the model is designed to mimic the data. The Add temporal derivative option does not modify the EV waveform. Instead, an additional column is added next to the EV corresponding to the temporal derivative of that particular EV waveform. Pressing on View design allows the visualization of the EV's and their derivatives. The orthogonality of the EV's can be further checked by displaying the Covariance.

The Covariance matrix is in fact comprised of two concatenated matrices. The first matrix is the absolute value of the normalized correlation of each EV with the rest of the EVs and as such not covariance in a strict sense. For well-conditioned design matrices that do not approach rank-deficiency, the diagonal elements are expected to be white and all other elements are expected to be darker. Off-diagonal bright covariance elements immediately draw attention to similar EV's. The covariance matrix is derived from the final design matrix which includes the user-defined EVs as well as any existing temporal



derivatives. When the first matrix is subjected to singular value decomposition (SVD), the neighboring second matrix is obtained consisting of the eigenvalues of the first matrix.

The Design matrix is a succinct summary of the experimental setup consisting of the EVs and Contrast specifications. (Figure 34-10). The vertical columnar representation at the top of the "Model" GUI is the Design matrix. The bottom horizontal rows of the "Model" GUI refer to the Contrast vectors. A different Z-statistic image is associated with each "Contrast" vector. The bar on the left of the "Design matrix" represents time, starting at the top and pointing downwards. The white marks show the position of every 10th volume in time. The red bar shows the period of the longest temporal cycle which has survived highpass filtering. The different columns of the "Design matrix" refer to the different explanatory variables e.g. stimulus types. When the Add temporal derivative option is selected as is the case for Figure 34-10, two columns are associated with each explanatory variable whereby the first column represents the EV waveform and the second represents the temporal derivative of the EV waveform. At the bottom, the horizontal contrast vector (often 1, 0, -1) gives the weighting of each explanatory variable. In the simplest case, to convert a single EV into a Z statistic image, the contrast value of that particular EV should be set to 1 and the contrast values for all other EVs should be set to 0. To specify more than one contrast vector, the Number of contrasts located within the Contrasts page should be modified. In the contrast specifications, there are no provisions made for the temporal derivatives. This does not imply though that the temporal derivatives are not used. On the contrary, they are used to account for latency between model and data.



Figure 34-10. The Design matrix and the contrast fields generated for an ABACABAC setup.

The F-tests specifications allow the user to investigate several contrasts at the same time. The F-test is a "generalization" of the t-test which can only handle a single contrast. With the F-test, the contribution of each contrast to the model can be compared and the significance of each can be assessed. For instance, when a particular stimulation is being represented by several basis functions i.e. several EVs, that have the same input function but different HRF convolutions, the relative weights of the basis functions do not have to be specified in an F-test. It is sufficient to specify the three contrasts [1 0 0], [0 1 0], [0 0 1]. It would in fact be erroneous to combine the three basis functions into a single contrast of [1 1 1] and use the t-test.



34.4.1.2 ANALYSES

First-level analysis

With FEAT, options exist for conducting the entire Full first-level analysis or subsets thereof. The details of the first-level analysis can be accessed from under Advanced options by pressing on the Full first-level analysis button (Figure 34-11). The choices range from No first level-analysis (registration and/or group stats only) to various combinations of Pre-stats, Stats, Contrasts Thresholding & Rendering options. When Pre-stats is selected, only the pre-processing steps specified on the Pre-stats page are carried out. With the Pre-stats + Stats option, the parameter estimates (PE's) help assess how well the combination of the EV waveforms specified by each contrast, model the data at individual voxels. The higher the parameter estimate, the better the fit. The Stats + Contrasts, Thresholding, Rendering options further allows significance levels to be calculated for the statistical images whereby voxels deemed significant are color rendered to enable visualization of activated regions, based on the specifications on the Thresholding & rendering page. It is possible to omit the computationally intensive parts of FEAT and run the final stages of the analysis by specifying the directory containing the raw statistical images. The corresponding options of No first-level analysis (registration and/or group stats only) or Contrasts, Thresholding, Rendering options are both displayed in brown to indicate the need for directory specification. The Select FEAT directory however is only functional when the Number of first-level analyses is greater than 0. With either one of these analyses, the Pre-stats notebook page is automatically grayed out and the paradigm specification options disappear from the GUI. The various notebook pages. are automatically enabled or disabled based on the type of analysis desired.

Details about the Pre-stats and Thresholding & rendering notebook pages are available in the following paragraphs. The choice of Registration is independent of the level of analysis however and Group stats should be selected only when a random effects or second-level analysis is desired. Details about different items on the GUI can be obtained by letting the arrow rest on a specific entry for some time. A prerequisite for this is that the Balloon help be selected under Misc.



Figure 34-11. The FEAT Advanced options along with the details of the Pre-stats page.

Pre-stats

The various options under Pre-stats (Figure 34-11) are carried out prior to the actual statistical calculations, to accurately localize activation foci. Prior to motion correction, intensity normalization, and brain mask generation, all images are background thresholded. The background threshold is automatically set to the product of the maximum input image intensity and Brain/background threshold % specification under Misc. Motion correction is normally applied to remove the effect of subject head motion during the experiment. The default motion correction option is MCFLIRT which uses FMRIB's Linear Registration Tool (FLIRT) optimized for FEAT to eliminate the effects of head motion during fMRI acquisitions, in a robust and accurate way. If motion correction has already been carried out, the None option should be selected. The BET brain extraction option ensures that the detected activations are located within the brain. With Spatial smoothing FWHM (mm), the smoothing is carried out on each fMRI volume separately. In order to reduce noise without reducing valid activation, the underlying activation area has to be larger than the extent of the smoothing. For very small activation areas the suggested value of 5 mm should



be further reduced. Intensity normalization forces every fMRI volume to have the same mean intensity. Initially, the mean intensity in each volume is calculated. Subsequently, all voxels within the volume are scaled by the global/mean intensity factor. This option is normally discouraged as it can be confounded by strong activation. Even in the absence of Intensity normalization however, grand-mean scaling is applied i.e. the whole 4D data set is normalized by a single scaling factor so that inter-subject and inter-session second-level analyses are valid. Finally with Temporal filtering, the data can be subjected to Highpass and/or Lowpass filtering. Standard linear highpass filtering is known to introduce new autocorrelation into the data. Hence, for the Highpass option, a nonlinear filter is adopted to remove the low frequency artifacts. The High pass filter cutoff in the main GUI should be selected such that the cutoff is not below that of the frequency of the block design, not to accidentally eliminate the signal of interest. For the Lowpass option, Gaussian smoothing is employed to reduce high-frequency noise. With the IRVA semi-model-free analyses however, Gaussian smoothing invalidates the statistics. So, in this case, FEAT applies only highpass filtering. In fact, in general, lowpass filtering is discouraged and this option is turned off by default. Thresholding and Rendering

Statistical calculations form the essence of fMRI analyses, yet they only become meaningful when analyzed for significance. To determine the voxels or clusters of voxels activated at a particular significance level, the statistical images are thresholded. There are several Thresholding options available in FEAT hidden underneath the Cluster button (Figure 34-12). The default Cluster thresholding employs a Z threshold to define contiguous clusters. The default value of 2.3 for this threshold can be increased for higher levels of activation. The estimated significance level of the surviving clusters is then compared with the Cluster P threshold. Clusters which are found to be significant in this comparison, are used to mask the original Z statistic image for subsequent rendering. Cluster thresholding is normally more sensitive to activation than the alternative multiple comparison correction method of Voxel thresholding. With the latter approach, multiple comparison resel correction is carried out with the threshold set to the level of the Corrected voxel P threshold. In resel correction, only the "independent" voxels are included in the calculation. Thus compared to Bonferroni correction which is carried out on all brain voxels, resel correction i.e. Voxel thresholding, is less conservative. A third thresholding option is the Uncorrected method which simply thresholds the Z statistic at the specified Uncorrected voxel P threshold. When thresholding is not applied to the statistical maps, i.e. None. option, it is not possible to differentiate between activated and non-activated voxels. Hence, there is nothing to render, as one might surmise from the disappearance of the Rendering options from the GUI. In general, the Rendering options complement the Thresholding options (Figure 34-12) by allowing the colored activation foci to be superimposed onto the brain. The activation foci can be rendered as Transparent blobs or Solid blobs depending on the specification In determining the colors, the default is to Use actual Z min/max values to assign red to the minimum Z statistic and yellow to the maximum Z statistic. In the case of more than one color rendered image e.g. for multiple contrasts, all Z static images are used to determine the overall



range of Z values. The Use preset Z min/max option further allows the min/max value to be set by hand. to ensure consistency of Z statistic color-coding when several experiments are reported side by side. In this case, the Min and Max values used for color coding should be selected conservatively (e.g. Min=1, Max=15), to avoid unintentional thresholding via color-rendering.

Figure 34-12. The FEAT Thresholding & rendering options available under first-level analysis

Multiple first-level analyses

Functional analyses are usually carried out on a single data set. If the first-level analysis is to be carried out on several data sets, the File-based first-level analyses at the very top of the GUI should be set to the required number of files. When File-based first-level analyses is 0, the analysis is conducted on the Group data occupying the display area. Pressing on the Select Group button reminds the user of this. When the File-based first-level analyses is greater than 0, the Select Group button changes into the Select 4D data button pressing on which allows the user to specify the filenames. All files are assumed to be in Analyze format. The same set of FEAT settings are used for each analysis. Hence it is expected that all files consist of the same number of time volumes. Multiple analyses take a considerable amount of time to run. Thus, it may be



preferable to conduct multiple analyses overnight. The time for the onset of the analysis can be specified through the Delay before starting (hours) entry under the Misc option.

34.4.1.3 SECOND-LEVEL ANALYSIS

Second-level analyses, also known as "multi-session" or "multi-subject analyses" allow meta-analyses of several first-level analyses whereby the prevalence of an effect in the population can be assessed. The Group stats option governs the second-level analysis and can be carried out on a single group of sessions/subjects or on two groups. Since more than one study is involved in a second-level analysis, the File-based first-level analyses is automatically set to the minimum 2 and Registration is automatically selected as well. Registration is mandatory as different subjects can only be compared when normalized to the same template. The Standard image refers to the template image which should ideally be in Talairach space with non-brain structures removed. The Subject's high resolution image is for the specification of the high-resolution anatomical image to which the functional images are registered. A separate high resolution image needs to be specified for each analysis. The high-resolution image is in turn registered to the standard brain. It is strongly recommended that the high resolution image have non-brain structures already removed through BET or an equivalent deskulling routine. The second-level analysis, also requires the specification of the Number in group A (control group) on the Group stats page. For a single group of sessions/subjects, Number in group A (control group) should be set to 0. For two-group analysis i.e. B-A test where group B (test group) is to be tested against group A (control group), Number in group A (control group) has to be set to a non-zero value. The assignment to the groups is then set according to the order of the files specified using Select 4D data with the group A sessions first followed by the group B sessions.

34.4.1.4 EVENT-RELATED ANALYSIS

Event-related acquisitions in FEAT can be analyzed using both the FILM model-based and IRVA semi-model-free approaches. For regularly spaced event-related acquisitions, the choice is not crucial. When the stimuli are randomly spaced however, custom-files should be used in conjunction with FILM, or the Triggered option should be selected for semi-model-free IRVA (Figure 34-13). With the latter, additional specifications have to be made for Volumes per event which includes baseline as well as activation and File of events text file which contains the start of each event specified on successive lines. As the semi-model-free approach of IRVA, does not require the specification of a waveform17.

34.4.2 Inter Repetition Variance Analysis The semi-model-free IRVA is not only available as part of the FEAT analysis option, but can also be independently accessed through Toolbox->FSL-> FMRI

17 Clare S, Humberstone M. Hukin J, Blumhardt L, Bowtell R, Morris P (1999). Detecting

Activation in Event-Related fMRI Using Analysis of Variance. Magn. Reson. Med. 42:1117-1122.



Analysis->Inter-Repetition Variance Analysis. In a semi-model-free analysis, the reference waveforms are not convolved with a model of the hemodynamic response, but the hemodynamic response is still taken into account with special regressors of no interest10. IRVA can be used to detect activations in fMRI data, without specifying the shape of the expected time course. IRVA is best suited to the type of experiment where the same stimulus is presented a number of times, and the shape of the response is the same following each presentation, but this shape is not predictable. The semi-model-free utility consists of the Cyclic and the Triggered options.

Figure 34-13. The FEAT semi-model-free GUI for possible event-related analysis

34.4.2.1 CYCLIC IRVA

The Cyclic IRVA option (Figure 34-14) is appropriate for paradigms consisting of many repetitions of the same stimulation cycle i.e. for ON/OFF blocks and for repeated single events of constant spacing. Initially, the images are thresholded such that F-scores are only calculated in regions containing the brain. The threshold is equal to the product of the specified Threshold (% of max): and the maximum signal intensity of the first image of the volume. The initial volumes acquired prior to reaching steady state can be ignored using the Ignore first n volumes: specification. The user also needs to specify the number of Volumes per complete cycle. The number of cycles in the data set is then computed from the total number of volumes in the group. The Timecourse option is only related to display and not to calculations. When the time series display is turned on while using IRVA, depending on the specification, all time points Whole, an average curve Average, or nothing None may be displayed. Two images are produced as a result of the cyclic analysis. the F-score map and the Z-score map. The Z-score map is computed initially by calculating on a voxel-by-voxel basis the p-value associated with each F-score. Subsequently the Z-scores associated with each p-value are calculated to construct the map.



Figure 34-14. The GUI for the Cyclic IRVA option

34.4.2.2 TRIGGERED IRVA

The Triggered IRVA option is appropriate for experiments having irregularly spaced activation cycles i.e. event-related experiments of non-cyclic origin (Figure 34-15). Again the images are thresholded prior to the analysis whereby the threshold is set to the product of the specified Threshold (% of max): and the maximum signal intensity of the first image of the volume. The user has to specify the Number of events:. The number of volumes following each event is then entered across Volumes per event:. This quantity roughly corresponds to the minimum number of baseline volumes. and should include the number of "rest" volumes as well as the volumes involved in activation to baseline transition. A text file has to be also specified across File of events: to indicate the start of each event. For the hypothetical case where a subject presses a button upon seeing a visual cue, if the visual cues are given at 6, 21, 42, 60, 78, and 102 seconds, and the repetition time TR is 3 seconds, the text file will consist of the numbers 2 7 14 20 26 34, one number per line. The Number of events would be 6. The Volumes per event would be 5 to include the 15 seconds following activation. The text file can be edited by pressing on the Edit button. Again, the Timecourse is for display purposes as explained in the Cyclic IRVA subsection.



Figure 34-15. The GUI for the Triggered IRVA option

34.4.3 Multivariate Exploratory Linear Optimised Decomposition into Independent Components In fMRI, activation related signal changes can have components in addition to the GM task related response. Depending on the acquisition parameters and on the subject, respiratory and cardiac contributions can modify the response in such a way that user-predefined waveforms may no longer adequately represent the response. It is also possible for compromised regions of the brain to have longer delays in regions of activation. In such cases, where the response does not conform to a pre-defined waveform, model-dependent approaches are bound to miss true regions of activation. Independent component analysis (ICA) is a model-free approach which detects regions of activation with no a priori assumptions. The ICA analysis in FSL is termed MELODIC which is an acronym for multivariate exploratory linear optimised decomposition into independent components. In subdividing the overall response into a specified number of independent temporal components, ICA takes into consideration the cumulative temporal response from all pixels. Associated with each temporal component, there is a separate spatial component which highlights the regions that have time courses matching that particular temporal signature. To determine the independent components, the MELODIC ICA tool uses a latent variables model xi(t) = ∑

kak(t)ski + εi(t) (6)



where xi is the observed temporal waveform at the ith voxel; k refers to the number of specified independent components with ak(t) referring to the time course and sk referring to the spatial map associated with the kth component; εi(t) is Gaussian noise. Note that the assumption of Gaussian noise does not interfere with the assumption of non-Gaussianity of xi(t) so essential to ICA. Unlike principal component analysis (PCA) which characterizes the data with second order statistics i.e. the covariance matrix, ICA is a higher order method that takes into consideration clustering and independence of the data18. When all voxels are considered simultaneously, Eq. 6 becomes X = A S + ε (7) whereby the observed matrix X is decomposed into the product of a mixing matix A and a matrix of underlying spatial components S using a fixed-point method that maximizes negentropy as a measure of non-Gaussianity16.

Figure 34-16. MELODIC User Interface

A crucial parameter in ICA analysis is the specification of the Number of output components. Performing ICA analysis with more than the optimal number of components can split the information leading to spurious results and to sub-optimal contrast in the spatial maps associated with the individual components. When too few components are specified, information that exists in ideally separable components is combined. Currently the default for the number of independent components is ¼ the number of temporal volumes. An automatic dimensionality estimation tool for a more sophisticated default specification is in the process of being developed by the FMRIB group.

The MELODIC program has additional Advanced Options for removing non-brain tissue as well as variance normalization. To optimize ICA calculations

18 Hyvarinen A. (1999). Fast and robust fixed point algorithms for Independent COmponent Analysis"

IEEE Trans. Neural Networks 10:626-634.



non-brain tissue has to be eliminated. The preferred method is to Use BET to mask data. Alternatively the user may perform simple masking by specifying a Threshold %. All temporal waveforms can also be constrained to have unit variance, by selecting Variance normalise timecourses. An option also exists to Smooth eigenvectors prior to ICA. At the end of the analysis, the time courses for each component and the associated spatial maps can be visualized by browsing the generated report. For the location of the report the user should refer to the window from which the MEDx program is run.

34.4.4 Statistics Colour Rendering The Statistics Colour Rendering option located in Toolbox->FSL->FMRI Analysis allows the statistical results within up to two separate files to be color-coded and superimposed onto the original image. The tool is similar to the Toolbox->Fusion->Colorwash option already in MEDx which allows the superposition of only one color-coded statistical file onto the original image. In Statistics Colour Rendering, the number of files entered across Number of stats images (Figure 34-17) determines the Stats image entries that are visible in the GUI. All Stats image and Background image files can be selected from the Page Manager using the Select button. For each file, only the values above the corresponding Min are included in the rendering. The Colourmap type: lets the user choose between Solid Colours and Transparent Colours. While the latter allows the background image to be seen through the Color blobs, the former precludes the visualization of the background. Note that rendering works best when Rendered image type is set to Integer; image works in 3D rendering. The option of Floating point; red-yellow blobs contain original stats values if solid Colours are used should only be selected for experimentation as the floating point representation does not work well for rendering. A prerequisite for Statistics Colour Rendering is for the statistics images to be registered to the background image.

Figure 34-17. The GUI for the Statistics Colour Rendering option



34.4.5 Percentage Noise Analysis

The Percentage Noise Analysis item in Toolbox->FSL->FMRI Analysis calculates the temporal mean (µ) and standard deviation (σ) images, and then finds the ratio σ/µ, providing a simple way of assessing noise. At the end of the analysis, the mean(σ/µ) and median(σ/µ) of the ratio are displayed as indices of noise. The user can conveniently exclude areas of no interest such as the edge of the brain by selecting the Ignore Edge Noise option. There is also an option for normalizing the images through the Normalize Intensity option.

34.5 MISCELLANEOUS FSL UTILITIES

The miscellaneous utilities in FSL are primarily included to maintain the integrity of the FSL package. Most of these utilities can also be implemented using generic MEDx tools. However, the existence of these utilities as compact menu items, can facilitate the analysis. The Misc Utilities are implicitly grouped together through the use of separators.

The first group of items constitute general FSL Utilities. The Make Image Isometric menu item allows the user to post-process images to obtain isometric voxels. This function is equivalent to the Toolbox->Transformation->Spatial (3D) option in MEDx and is simply retained to preserve FSL integrity. The Concat Transforms option is designed for the concatenation of registration transform files. The transform files, First Transform and Second Transform as well as the transform types the First Transform Type and the Second Transform Type can all be specified from the GUI. The existing options for transform type are: Talairach, AIR, Reslice, Resample, and User specified. The output of the concatenation is saved in the specified Output Transform filename. The Set Origin option allows the user to set the coordinate system of an image (the To Volume) with respect to the coordinates of another image (the From Volume). The specifications are made by accessing the Page Manager through the Select buttons. The Delayed Folder Saving option provides the user with the opportunity of saving a folder after a specified delay as saving folders can be quite time-consuming. The Folder Name to save in can be explicitly specified along with the wait across Enter delay in hours before save:. The MEDx session can be terminated at the end of the save by selecting Kill MEDx after saving folder?. Note that a Save Folder option already exists in the MEDx Folder menu, albeit without provisions for delay.

The second group of items are utilities that can only be applied to groups i.e. 4D images which have a temporal dimension. The Group Median image option generates a volume such that each voxel within the volume has a value corresponding to the temporal median of all voxels occupying that specific location. The Group Do (Generic Operation), provides the user with web instructions on how to apply MEDx scripting commands to groups in general. The Scatter Plot from 2 Images utility takes the first two images of the current Group page i.e. the first two temporal volumes and performs a 2D scatter plot on all slices included in those volumes. The scatter plot is automatically displayed



with the signal intensity ranges of each volume constituting the minimum and the maximum of the abscissa and the ordinate, respectively.

The third group of items are in a sense graphics/output items in that the result of the operation is viewed in the display area regardless of whether the operation has been conducted on a group or a volume. The Cut 3D Corner utility instructs the user to draw a rectangular graphic. It then applies the graphic to subsequent slices and erases the region within these graphics. Cut 3D Corner is retained solely to preserve FSL integrity. A far more comprehensive utility exists in MEDx whereby the region within any closed graphics can be erased using the Graphic->Graphic Region->Erase Inside Region option. The Rendering Animation option provides the user with web instructions on how to create an animation script. The Rolling Display is equivalent to a group movie. The frame rate is specified through the Delay between pages (seconds) entry and the Number of repetitions determines the total number of cycles. It is a convenient tool for visualizing the temporal progression of the acquisition within all slices simultaneously.

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